source("global.R")
library(ggrepel)
lipid <- readRDS("../data/untargeted_singletp_fin3_001.rds")$quantHDL lipidome quantification
Analysis based on the targeted lipidomic results. File: mx 733759_Agus_lipidomics_human plasma lipoproteins_07-2023 submit.xlsx. The peak heights were converted to ng/mL based on the amount and peak height of iSTD.
Data processing
Features with peak heights in blanks greater than or close to those in pooled samples were excluded. Identified by the pval comparing blank samples to pooled samples, p<0.01.
Features with InChI Keys that couldn’t be found in the PubChem were excluded.
Compounds with identical InChI Keys were selected by lowest CV in pooled samples.
Assign lipid class from LIPID MAPS and manually curated table.
istds were selected by the minimun p-values from the the comparison between blank samples and pooled samples.
Concentration were calculated based the istd with most similar chain length.
Cholesterol istd is missing. Used CE 16:1-d7
CVs of pooled samples
The CVs of pooled samples for each lipid species were calculated using the following formula:
\[ CV_{lipid\ species} = SD_{lipid\ species}/Mean_{lipid\ species} * 100\% \]
We are less confident with the lipid species with a high CV. These lipid species will be excluded for further analysis.
hist(lipid$fdata$qc_cv, main = "The distribution of CVs")
sum(lipid$fdata$qc_cv>30)[1] 0We excluded lipid species with a CV > 30%. 0 features were excluded.
lipid <- subset_features(lipid, lipid$fdata$qc_cv<30)The hierarchical relationship of lipid classes.
Following analysis included all quantified lipid species.
Included all quantified lipid species. 347 lipid species for analysis.
Lipid class
Pie chart
lipid.class3 <- subset_features(lipid, !is.na(lipid$fdata$
class))
lipid.class3 <- summarize_feature(lipid.class3, "class")
edata.prop3 <- apply(lipid.class3$edata, 2, function(col){
col/sum(col)*100
})
# pie chart
pies.class3 <- lapply(sampleNames(lipid), plotPie, edata.prop3)
legend.class3 <- get_legend(pies.class3[[1]]+theme(legend.position = "bottom"))
pies.class3 <- lapply(pies.class3, function(x)x+theme(legend.position = "none"))
plot_grid(plotlist = pies.class3, nrow = 3)
Bar plot
# bar plot
plotBar(edata.prop3)
PCA
edata.scaled3 <- scale(t(edata.prop3), center = T)
res.pca <- PCA(edata.scaled3, scale.unit = T, graph = FALSE)
# fviz_eig(res.pca, addlabels = TRUE)
fviz_pca_ind(res.pca,
axes = c(1,2),
repel = TRUE,
title = NULL
) +
theme_cynthia_bw() +
theme(
legend.position = "none"
) +
labs(title = NULL)
Lipid main class
Pie chart
lipid.class2 <- subset_features(lipid, !is.na(lipid$fdata$Main.class))
lipid.class2 <- summarize_feature(lipid.class2, "Main.class")
edata.prop2 <- apply(lipid.class2$edata, 2, function(col){
col/sum(col)*100
})
# pie chart
pies.class2 <- lapply(sampleNames(lipid), plotPie, edata.prop2)
legend.class2 <- get_legend(pies.class2[[1]])
pies.class2 <- lapply(pies.class2, function(x)x+theme(legend.position = "none"))
plot_grid(plotlist = pies.class2, nrow = 3)
Bar plot
# bar plot
plotBar(edata.prop2)
Lipid category
Pie chart
# edata
lipid.class <- subset_features(lipid, !is.na(lipid$fdata$Categories))
lipid.class <- summarize_feature(lipid.class, "Categories")
edata.prop <- apply(lipid.class$edata, 2, function(col){
col/sum(col)*100
})
# pie chart
pies.class1 <- lapply(sampleNames(lipid), plotPie, edata.prop)
legend.class1 <- get_legend(pies.class1[[1]])
pies.class1 <- lapply(pies.class1, function(x)x+theme(legend.position = "none"))
plot_grid(plotlist = pies.class1, nrow = 3) 
Bar plot
# bar plot
plotBar(edata.prop)
Following analysis only included lipids curated in LIPID MAPS.
184 lipid species for the analysis.
LM: sub class
Pie chart
lipid.class.sub <- subset_features(lipid, !is.na(lipid$fdata$LM_sub_class))
lipid.class.sub <- summarize_feature(lipid.class.sub, "LM_sub_class")
edata.prop.sub <- apply(lipid.class.sub$edata, 2, function(col){
col/sum(col)*100
})
# pie chart
pies.class.sub <- lapply(sampleNames(lipid), plotPie, edata.prop.sub)
legend.class.sub <- get_legend(pies.class.sub[[1]]+theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 5)))
pies.class.sub <- lapply(pies.class.sub, function(x)x+theme(legend.position = "none"))
plot_grid(plotlist = pies.class.sub, nrow = 3) %>%
plot_grid(legend.class.sub, nrow = 2, rel_heights = c(3,1))
Bar plot
# bar plot
plotBar(edata.prop.sub)
PCA plot
edata.scaled.sub <- scale(t(edata.prop.sub), center = T)
res.pca <- PCA(edata.scaled.sub, scale.unit = T, graph = FALSE)
# fviz_eig(res.pca, addlabels = TRUE)
fviz_pca_ind(res.pca,
axes = c(1,2),
repel = TRUE,
title = NULL
) +
theme_cynthia_bw() +
theme(
legend.position = "none"
) +
labs(title = NULL)
LM: main class
Pie chart
lipid.class.main <- subset_features(lipid, !is.na(lipid$fdata$
LM_main_class))
lipid.class.main <- summarize_feature(lipid.class.main, "LM_main_class")
edata.prop.main <- apply(lipid.class.main$edata, 2, function(col){
col/sum(col)*100
})
# pie chart
pies.class.main <- lapply(sampleNames(lipid), plotPie, edata.prop.main)
legend.class.main <- get_legend(pies.class.main[[1]]+theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 3)))
pies.class.main <- lapply(pies.class.main, function(x)x+theme(legend.position = "none"))
plot_grid(plotlist = pies.class.main, nrow = 3) %>%
plot_grid(legend.class.main, nrow = 2, rel_heights = c(4,1))
Bar plot
# bar plot
plotBar(edata.prop.main)
LM: core
Pie chart
lipid.class.core <- subset_features(lipid, !is.na(lipid$fdata$LM_core))
lipid.class.core <- summarize_feature(lipid.class.core, "LM_core")
edata.prop.core <- apply(lipid.class.core$edata, 2, function(col){
col/sum(col)*100
})
# pie chart
pies.class.core <- lapply(sampleNames(lipid), plotPie, edata.prop.core)
legend.class.core <- get_legend(pies.class.core[[1]])
pies.class.core <- lapply(pies.class.core, function(x)x+theme(legend.position = "none"))
plot_grid(plotlist = pies.class.core, nrow = 3)
Bar plot
# bar plot
plotBar(edata.prop.core)